Overview

Dataset statistics

Number of variables26
Number of observations6080
Missing cells11618
Missing cells (%)7.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory237.7 B

Variable types

Categorical15
Numeric10
Unsupported1

Alerts

comp has constant value "La Liga"Constant
match report has constant value "Match Report"Constant
date has a high cardinality: 1050 distinct valuesHigh cardinality
captain has a high cardinality: 306 distinct valuesHigh cardinality
pk is highly imbalanced (72.0%)Imbalance
pkatt is highly imbalanced (66.1%)Imbalance
xg has 1520 (25.0%) missing valuesMissing
xga has 1520 (25.0%) missing valuesMissing
attendance has 976 (16.1%) missing valuesMissing
notes has 6080 (100.0%) missing valuesMissing
dist has 1522 (25.0%) missing valuesMissing
round is uniformly distributedUniform
venue is uniformly distributedUniform
notes is an unsupported type, check if it needs cleaning or further analysisUnsupported
gf has 1740 (28.6%) zerosZeros
ga has 1740 (28.6%) zerosZeros
sot has 198 (3.3%) zerosZeros

Reproduction

Analysis started2023-08-13 23:48:53.138372
Analysis finished2023-08-13 23:49:08.042310
Duration14.9 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

date
Categorical

Distinct1050
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
2023-06-04
 
20
2019-05-12
 
20
2020-07-16
 
20
2020-07-19
 
20
2021-05-16
 
20
Other values (1045)
5980 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters60800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-08-13
2nd row2022-08-21
3rd row2022-08-28
4th row2022-09-03
5th row2022-09-10

Common Values

ValueCountFrequency (%)
2023-06-04 20
 
0.3%
2019-05-12 20
 
0.3%
2020-07-16 20
 
0.3%
2020-07-19 20
 
0.3%
2021-05-16 20
 
0.3%
2016-05-08 20
 
0.3%
2023-05-28 18
 
0.3%
2022-05-15 18
 
0.3%
2015-12-30 18
 
0.3%
2021-04-18 16
 
0.3%
Other values (1040) 5890
96.9%

Length

2023-08-13T19:49:08.118152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-06-04 20
 
0.3%
2020-07-16 20
 
0.3%
2020-07-19 20
 
0.3%
2021-05-16 20
 
0.3%
2016-05-08 20
 
0.3%
2019-05-12 20
 
0.3%
2023-05-28 18
 
0.3%
2022-05-15 18
 
0.3%
2015-12-30 18
 
0.3%
2019-05-18 16
 
0.3%
Other values (1040) 5890
96.9%

Most occurring characters

ValueCountFrequency (%)
0 14216
23.4%
2 13464
22.1%
- 12160
20.0%
1 9706
16.0%
9 2030
 
3.3%
3 1900
 
3.1%
8 1712
 
2.8%
7 1454
 
2.4%
6 1418
 
2.3%
5 1400
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48640
80.0%
Dash Punctuation 12160
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14216
29.2%
2 13464
27.7%
1 9706
20.0%
9 2030
 
4.2%
3 1900
 
3.9%
8 1712
 
3.5%
7 1454
 
3.0%
6 1418
 
2.9%
5 1400
 
2.9%
4 1340
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 12160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14216
23.4%
2 13464
22.1%
- 12160
20.0%
1 9706
16.0%
9 2030
 
3.3%
3 1900
 
3.1%
8 1712
 
2.8%
7 1454
 
2.4%
6 1418
 
2.3%
5 1400
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14216
23.4%
2 13464
22.1%
- 12160
20.0%
1 9706
16.0%
9 2030
 
3.3%
3 1900
 
3.1%
8 1712
 
2.8%
7 1454
 
2.4%
6 1418
 
2.3%
5 1400
 
2.3%

time
Categorical

Distinct30
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
21:00
1080 
18:30
936 
16:15
702 
20:45
452 
14:00
392 
Other values (25)
2518 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters30400
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21:00
2nd row22:00
3rd row19:30
4th row21:00
5th row18:30

Common Values

ValueCountFrequency (%)
21:00 1080
17.8%
18:30 936
15.4%
16:15 702
11.5%
20:45 452
 
7.4%
14:00 392
 
6.4%
12:00 296
 
4.9%
20:30 260
 
4.3%
16:00 246
 
4.0%
19:30 238
 
3.9%
22:00 238
 
3.9%
Other values (20) 1240
20.4%

Length

2023-08-13T19:49:08.221906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21:00 1080
17.8%
18:30 936
15.4%
16:15 702
11.5%
20:45 452
 
7.4%
14:00 392
 
6.4%
12:00 296
 
4.9%
20:30 260
 
4.3%
16:00 246
 
4.0%
19:30 238
 
3.9%
22:00 238
 
3.9%
Other values (20) 1240
20.4%

Most occurring characters

ValueCountFrequency (%)
0 8438
27.8%
: 6080
20.0%
1 5808
19.1%
2 3106
 
10.2%
3 1860
 
6.1%
5 1536
 
5.1%
8 1132
 
3.7%
6 954
 
3.1%
4 888
 
2.9%
9 454
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24320
80.0%
Other Punctuation 6080
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8438
34.7%
1 5808
23.9%
2 3106
 
12.8%
3 1860
 
7.6%
5 1536
 
6.3%
8 1132
 
4.7%
6 954
 
3.9%
4 888
 
3.7%
9 454
 
1.9%
7 144
 
0.6%
Other Punctuation
ValueCountFrequency (%)
: 6080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8438
27.8%
: 6080
20.0%
1 5808
19.1%
2 3106
 
10.2%
3 1860
 
6.1%
5 1536
 
5.1%
8 1132
 
3.7%
6 954
 
3.1%
4 888
 
2.9%
9 454
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8438
27.8%
: 6080
20.0%
1 5808
19.1%
2 3106
 
10.2%
3 1860
 
6.1%
5 1536
 
5.1%
8 1132
 
3.7%
6 954
 
3.1%
4 888
 
2.9%
9 454
 
1.5%

comp
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
La Liga
6080 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters42560
Distinct characters5
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLa Liga
2nd rowLa Liga
3rd rowLa Liga
4th rowLa Liga
5th rowLa Liga

Common Values

ValueCountFrequency (%)
La Liga 6080
100.0%

Length

2023-08-13T19:49:08.308677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:49:08.393988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
la 6080
50.0%
liga 6080
50.0%

Most occurring characters

ValueCountFrequency (%)
L 12160
28.6%
a 12160
28.6%
6080
14.3%
i 6080
14.3%
g 6080
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 24320
57.1%
Uppercase Letter 12160
28.6%
Space Separator 6080
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12160
50.0%
i 6080
25.0%
g 6080
25.0%
Uppercase Letter
ValueCountFrequency (%)
L 12160
100.0%
Space Separator
ValueCountFrequency (%)
6080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 36480
85.7%
Common 6080
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 12160
33.3%
a 12160
33.3%
i 6080
16.7%
g 6080
16.7%
Common
ValueCountFrequency (%)
6080
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 12160
28.6%
a 12160
28.6%
6080
14.3%
i 6080
14.3%
g 6080
14.3%

round
Categorical

Distinct38
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
Matchweek 1
 
160
Matchweek 29
 
160
Matchweek 22
 
160
Matchweek 23
 
160
Matchweek 24
 
160
Other values (33)
5280 

Length

Max length12
Median length12
Mean length11.763158
Min length11

Characters and Unicode

Total characters71520
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMatchweek 1
2nd rowMatchweek 2
3rd rowMatchweek 3
4th rowMatchweek 4
5th rowMatchweek 5

Common Values

ValueCountFrequency (%)
Matchweek 1 160
 
2.6%
Matchweek 29 160
 
2.6%
Matchweek 22 160
 
2.6%
Matchweek 23 160
 
2.6%
Matchweek 24 160
 
2.6%
Matchweek 25 160
 
2.6%
Matchweek 26 160
 
2.6%
Matchweek 27 160
 
2.6%
Matchweek 28 160
 
2.6%
Matchweek 30 160
 
2.6%
Other values (28) 4480
73.7%

Length

2023-08-13T19:49:08.466388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
matchweek 6080
50.0%
9 160
 
1.3%
19 160
 
1.3%
3 160
 
1.3%
4 160
 
1.3%
5 160
 
1.3%
6 160
 
1.3%
7 160
 
1.3%
8 160
 
1.3%
10 160
 
1.3%
Other values (29) 4640
38.2%

Most occurring characters

ValueCountFrequency (%)
e 12160
17.0%
M 6080
8.5%
t 6080
8.5%
c 6080
8.5%
h 6080
8.5%
w 6080
8.5%
k 6080
8.5%
6080
8.5%
a 6080
8.5%
2 2240
 
3.1%
Other values (9) 8480
11.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 48640
68.0%
Decimal Number 10720
 
15.0%
Uppercase Letter 6080
 
8.5%
Space Separator 6080
 
8.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2240
20.9%
1 2240
20.9%
3 2080
19.4%
4 640
 
6.0%
5 640
 
6.0%
6 640
 
6.0%
7 640
 
6.0%
8 640
 
6.0%
9 480
 
4.5%
0 480
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
e 12160
25.0%
t 6080
12.5%
c 6080
12.5%
h 6080
12.5%
w 6080
12.5%
k 6080
12.5%
a 6080
12.5%
Uppercase Letter
ValueCountFrequency (%)
M 6080
100.0%
Space Separator
ValueCountFrequency (%)
6080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54720
76.5%
Common 16800
 
23.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6080
36.2%
2 2240
 
13.3%
1 2240
 
13.3%
3 2080
 
12.4%
4 640
 
3.8%
5 640
 
3.8%
6 640
 
3.8%
7 640
 
3.8%
8 640
 
3.8%
9 480
 
2.9%
Latin
ValueCountFrequency (%)
e 12160
22.2%
M 6080
11.1%
t 6080
11.1%
c 6080
11.1%
h 6080
11.1%
w 6080
11.1%
k 6080
11.1%
a 6080
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12160
17.0%
M 6080
8.5%
t 6080
8.5%
c 6080
8.5%
h 6080
8.5%
w 6080
8.5%
k 6080
8.5%
6080
8.5%
a 6080
8.5%
2 2240
 
3.1%
Other values (9) 8480
11.9%

day
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
Sun
2348 
Sat
2070 
Fri
484 
Mon
380 
Wed
360 
Other values (2)
438 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18240
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSat
2nd rowSun
3rd rowSun
4th rowSat
5th rowSat

Common Values

ValueCountFrequency (%)
Sun 2348
38.6%
Sat 2070
34.0%
Fri 484
 
8.0%
Mon 380
 
6.2%
Wed 360
 
5.9%
Thu 228
 
3.8%
Tue 210
 
3.5%

Length

2023-08-13T19:49:08.551645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:49:08.659873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
sun 2348
38.6%
sat 2070
34.0%
fri 484
 
8.0%
mon 380
 
6.2%
wed 360
 
5.9%
thu 228
 
3.8%
tue 210
 
3.5%

Most occurring characters

ValueCountFrequency (%)
S 4418
24.2%
u 2786
15.3%
n 2728
15.0%
a 2070
11.3%
t 2070
11.3%
e 570
 
3.1%
F 484
 
2.7%
r 484
 
2.7%
i 484
 
2.7%
T 438
 
2.4%
Other values (5) 1708
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12160
66.7%
Uppercase Letter 6080
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 2786
22.9%
n 2728
22.4%
a 2070
17.0%
t 2070
17.0%
e 570
 
4.7%
r 484
 
4.0%
i 484
 
4.0%
o 380
 
3.1%
d 360
 
3.0%
h 228
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
S 4418
72.7%
F 484
 
8.0%
T 438
 
7.2%
M 380
 
6.2%
W 360
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 18240
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 4418
24.2%
u 2786
15.3%
n 2728
15.0%
a 2070
11.3%
t 2070
11.3%
e 570
 
3.1%
F 484
 
2.7%
r 484
 
2.7%
i 484
 
2.7%
T 438
 
2.4%
Other values (5) 1708
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 4418
24.2%
u 2786
15.3%
n 2728
15.0%
a 2070
11.3%
t 2070
11.3%
e 570
 
3.1%
F 484
 
2.7%
r 484
 
2.7%
i 484
 
2.7%
T 438
 
2.4%
Other values (5) 1708
 
9.4%

venue
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
Home
3040 
Away
3040 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters24320
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowAway
3rd rowHome
4th rowAway
5th rowAway

Common Values

ValueCountFrequency (%)
Home 3040
50.0%
Away 3040
50.0%

Length

2023-08-13T19:49:08.758158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:49:08.847437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
home 3040
50.0%
away 3040
50.0%

Most occurring characters

ValueCountFrequency (%)
H 3040
12.5%
o 3040
12.5%
m 3040
12.5%
e 3040
12.5%
A 3040
12.5%
w 3040
12.5%
a 3040
12.5%
y 3040
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 18240
75.0%
Uppercase Letter 6080
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 3040
16.7%
m 3040
16.7%
e 3040
16.7%
w 3040
16.7%
a 3040
16.7%
y 3040
16.7%
Uppercase Letter
ValueCountFrequency (%)
H 3040
50.0%
A 3040
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24320
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 3040
12.5%
o 3040
12.5%
m 3040
12.5%
e 3040
12.5%
A 3040
12.5%
w 3040
12.5%
a 3040
12.5%
y 3040
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 3040
12.5%
o 3040
12.5%
m 3040
12.5%
e 3040
12.5%
A 3040
12.5%
w 3040
12.5%
a 3040
12.5%
y 3040
12.5%

result
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
W
2249 
L
2249 
D
1582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6080
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowW
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
W 2249
37.0%
L 2249
37.0%
D 1582
26.0%

Length

2023-08-13T19:49:08.920245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:49:09.008527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
w 2249
37.0%
l 2249
37.0%
d 1582
26.0%

Most occurring characters

ValueCountFrequency (%)
W 2249
37.0%
L 2249
37.0%
D 1582
26.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6080
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
W 2249
37.0%
L 2249
37.0%
D 1582
26.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6080
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
W 2249
37.0%
L 2249
37.0%
D 1582
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6080
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
W 2249
37.0%
L 2249
37.0%
D 1582
26.0%

gf
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3106908
Minimum0
Maximum10
Zeros1740
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:09.085832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2182541
Coefficient of variation (CV)0.9294748
Kurtosis1.8087349
Mean1.3106908
Median Absolute Deviation (MAD)1
Skewness1.1314028
Sum7969
Variance1.484143
MonotonicityNot monotonic
2023-08-13T19:49:09.167160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2094
34.4%
0 1740
28.6%
2 1368
22.5%
3 537
 
8.8%
4 225
 
3.7%
5 80
 
1.3%
6 29
 
0.5%
7 4
 
0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 1740
28.6%
1 2094
34.4%
2 1368
22.5%
3 537
 
8.8%
4 225
 
3.7%
5 80
 
1.3%
6 29
 
0.5%
7 4
 
0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
0.1%
6 29
 
0.5%
5 80
 
1.3%
4 225
 
3.7%
3 537
 
8.8%
2 1368
22.5%
1 2094
34.4%
0 1740
28.6%

ga
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3106908
Minimum0
Maximum10
Zeros1740
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:09.244566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2182541
Coefficient of variation (CV)0.9294748
Kurtosis1.8087349
Mean1.3106908
Median Absolute Deviation (MAD)1
Skewness1.1314028
Sum7969
Variance1.484143
MonotonicityNot monotonic
2023-08-13T19:49:09.320370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 2094
34.4%
0 1740
28.6%
2 1368
22.5%
3 537
 
8.8%
4 225
 
3.7%
5 80
 
1.3%
6 29
 
0.5%
7 4
 
0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 1740
28.6%
1 2094
34.4%
2 1368
22.5%
3 537
 
8.8%
4 225
 
3.7%
5 80
 
1.3%
6 29
 
0.5%
7 4
 
0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 2
 
< 0.1%
7 4
 
0.1%
6 29
 
0.5%
5 80
 
1.3%
4 225
 
3.7%
3 537
 
8.8%
2 1368
22.5%
1 2094
34.4%
0 1740
28.6%

opponent
Categorical

Distinct30
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
Atlético Madrid
 
304
Celta Vigo
 
304
Real Sociedad
 
304
Valencia
 
304
Athletic Club
 
304
Other values (25)
4560 

Length

Max length15
Median length11
Mean length8.7625
Min length5

Characters and Unicode

Total characters53276
Distinct characters41
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRayo Vallecano
2nd rowReal Sociedad
3rd rowValladolid
4th rowSevilla
5th rowCádiz

Common Values

ValueCountFrequency (%)
Atlético Madrid 304
 
5.0%
Celta Vigo 304
 
5.0%
Real Sociedad 304
 
5.0%
Valencia 304
 
5.0%
Athletic Club 304
 
5.0%
Betis 304
 
5.0%
Real Madrid 304
 
5.0%
Villarreal 304
 
5.0%
Sevilla 304
 
5.0%
Barcelona 304
 
5.0%
Other values (20) 3040
50.0%

Length

2023-08-13T19:49:09.405714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
madrid 608
 
7.5%
real 608
 
7.5%
athletic 304
 
3.8%
barcelona 304
 
3.8%
sevilla 304
 
3.8%
villarreal 304
 
3.8%
club 304
 
3.8%
atlético 304
 
3.8%
valencia 304
 
3.8%
sociedad 304
 
3.8%
Other values (27) 4408
54.7%

Most occurring characters

ValueCountFrequency (%)
a 8284
15.5%
l 5814
 
10.9%
e 4560
 
8.6%
i 3800
 
7.1%
d 2432
 
4.6%
r 2394
 
4.5%
t 2394
 
4.5%
o 2356
 
4.4%
n 2052
 
3.9%
1976
 
3.7%
Other values (31) 17214
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43244
81.2%
Uppercase Letter 8056
 
15.1%
Space Separator 1976
 
3.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8284
19.2%
l 5814
13.4%
e 4560
10.5%
i 3800
8.8%
d 2432
 
5.6%
r 2394
 
5.5%
t 2394
 
5.5%
o 2356
 
5.4%
n 2052
 
4.7%
c 1976
 
4.6%
Other values (17) 7182
16.6%
Uppercase Letter
ValueCountFrequency (%)
V 1216
15.1%
A 874
10.8%
M 836
10.4%
C 836
10.4%
R 760
9.4%
S 684
8.5%
G 646
8.0%
L 608
7.5%
B 608
7.5%
E 608
7.5%
Other values (3) 380
 
4.7%
Space Separator
ValueCountFrequency (%)
1976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 51300
96.3%
Common 1976
 
3.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8284
16.1%
l 5814
 
11.3%
e 4560
 
8.9%
i 3800
 
7.4%
d 2432
 
4.7%
r 2394
 
4.7%
t 2394
 
4.7%
o 2356
 
4.6%
n 2052
 
4.0%
c 1976
 
3.9%
Other values (30) 15238
29.7%
Common
ValueCountFrequency (%)
1976
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52136
97.9%
None 1140
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8284
15.9%
l 5814
 
11.2%
e 4560
 
8.7%
i 3800
 
7.3%
d 2432
 
4.7%
r 2394
 
4.6%
t 2394
 
4.6%
o 2356
 
4.5%
n 2052
 
3.9%
1976
 
3.8%
Other values (26) 16074
30.8%
None
ValueCountFrequency (%)
é 684
60.0%
á 228
 
20.0%
ñ 114
 
10.0%
ó 76
 
6.7%
í 38
 
3.3%

xg
Real number (ℝ)

Distinct53
Distinct (%)1.2%
Missing1520
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean1.2822149
Minimum0
Maximum5.6
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:09.505054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.7
median1.1
Q31.7
95-th percentile2.7
Maximum5.6
Range5.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77164996
Coefficient of variation (CV)0.60181016
Kurtosis1.5148421
Mean1.2822149
Median Absolute Deviation (MAD)0.5
Skewness1.0552209
Sum5846.9
Variance0.59544366
MonotonicityNot monotonic
2023-08-13T19:49:09.613396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 276
 
4.5%
0.8 263
 
4.3%
1.2 258
 
4.2%
1 255
 
4.2%
0.7 253
 
4.2%
0.6 251
 
4.1%
0.5 242
 
4.0%
1.1 228
 
3.8%
0.4 223
 
3.7%
1.3 213
 
3.5%
Other values (43) 2098
34.5%
(Missing) 1520
25.0%
ValueCountFrequency (%)
0 5
 
0.1%
0.1 40
 
0.7%
0.2 119
2.0%
0.3 131
2.2%
0.4 223
3.7%
0.5 242
4.0%
0.6 251
4.1%
0.7 253
4.2%
0.8 263
4.3%
0.9 276
4.5%
ValueCountFrequency (%)
5.6 1
< 0.1%
5.3 1
< 0.1%
5.1 1
< 0.1%
4.9 1
< 0.1%
4.8 1
< 0.1%
4.7 1
< 0.1%
4.6 2
< 0.1%
4.5 2
< 0.1%
4.4 2
< 0.1%
4.3 2
< 0.1%

xga
Real number (ℝ)

Distinct53
Distinct (%)1.2%
Missing1520
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean1.2822149
Minimum0
Maximum5.6
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:09.732176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.7
median1.1
Q31.7
95-th percentile2.7
Maximum5.6
Range5.6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.77164996
Coefficient of variation (CV)0.60181016
Kurtosis1.5148421
Mean1.2822149
Median Absolute Deviation (MAD)0.5
Skewness1.0552209
Sum5846.9
Variance0.59544366
MonotonicityNot monotonic
2023-08-13T19:49:09.841401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 276
 
4.5%
0.8 263
 
4.3%
1.2 258
 
4.2%
1 255
 
4.2%
0.7 253
 
4.2%
0.6 251
 
4.1%
0.5 242
 
4.0%
1.1 228
 
3.8%
0.4 223
 
3.7%
1.3 213
 
3.5%
Other values (43) 2098
34.5%
(Missing) 1520
25.0%
ValueCountFrequency (%)
0 5
 
0.1%
0.1 40
 
0.7%
0.2 119
2.0%
0.3 131
2.2%
0.4 223
3.7%
0.5 242
4.0%
0.6 251
4.1%
0.7 253
4.2%
0.8 263
4.3%
0.9 276
4.5%
ValueCountFrequency (%)
5.6 1
< 0.1%
5.3 1
< 0.1%
5.1 1
< 0.1%
4.9 1
< 0.1%
4.8 1
< 0.1%
4.7 1
< 0.1%
4.6 2
< 0.1%
4.5 2
< 0.1%
4.4 2
< 0.1%
4.3 2
< 0.1%

poss
Real number (ℝ)

Distinct65
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.011349
Minimum18
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:09.964628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile32
Q142
median50
Q358
95-th percentile68
Maximum82
Range64
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.125865
Coefficient of variation (CV)0.2224668
Kurtosis-0.41283095
Mean50.011349
Median Absolute Deviation (MAD)8
Skewness-0.00026194006
Sum304069
Variance123.78487
MonotonicityNot monotonic
2023-08-13T19:49:10.075849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 223
 
3.7%
51 219
 
3.6%
54 216
 
3.6%
46 213
 
3.5%
52 210
 
3.5%
48 205
 
3.4%
45 200
 
3.3%
55 197
 
3.2%
50 190
 
3.1%
56 186
 
3.1%
Other values (55) 4021
66.1%
ValueCountFrequency (%)
18 3
 
< 0.1%
19 5
 
0.1%
20 3
 
< 0.1%
21 8
 
0.1%
22 4
 
0.1%
23 7
 
0.1%
24 13
0.2%
25 17
0.3%
26 17
0.3%
27 27
0.4%
ValueCountFrequency (%)
82 3
 
< 0.1%
81 5
 
0.1%
80 3
 
< 0.1%
79 8
 
0.1%
78 4
 
0.1%
77 8
 
0.1%
76 12
0.2%
75 17
0.3%
74 18
0.3%
73 26
0.4%

attendance
Real number (ℝ)

Distinct2458
Distinct (%)48.2%
Missing976
Missing (%)16.1%
Infinite0
Infinite (%)0.0%
Mean27422.964
Minimum13
Maximum98485
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:10.197039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile6557.15
Q114131.25
median19872
Q337792
95-th percentile69877.1
Maximum98485
Range98472
Interquartile range (IQR)23660.75

Descriptive statistics

Standard deviation19003.476
Coefficient of variation (CV)0.69297671
Kurtosis1.1148889
Mean27422.964
Median Absolute Deviation (MAD)8599
Skewness1.2828629
Sum1.3996681 × 108
Variance3.6113208 × 108
MonotonicityNot monotonic
2023-08-13T19:49:10.315310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10922 10
 
0.2%
11454 10
 
0.2%
19840 8
 
0.1%
10958 6
 
0.1%
9231 6
 
0.1%
16783 6
 
0.1%
19832 4
 
0.1%
23745 4
 
0.1%
16603 4
 
0.1%
17000 4
 
0.1%
Other values (2448) 5042
82.9%
(Missing) 976
 
16.1%
ValueCountFrequency (%)
13 2
< 0.1%
200 2
< 0.1%
583 2
< 0.1%
1738 2
< 0.1%
2689 2
< 0.1%
2896 2
< 0.1%
3280 2
< 0.1%
3518 2
< 0.1%
3576 2
< 0.1%
3592 2
< 0.1%
ValueCountFrequency (%)
98485 2
< 0.1%
97939 2
< 0.1%
95745 2
< 0.1%
94990 2
< 0.1%
93426 2
< 0.1%
93265 2
< 0.1%
92795 2
< 0.1%
92605 2
< 0.1%
92453 2
< 0.1%
91917 2
< 0.1%

captain
Categorical

Distinct306
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
Hugo Mallo
 
205
Sergio Ramos
 
155
Jesús Navas
 
143
Daniel Parejo
 
140
José Luis Morales
 
139
Other values (301)
5298 

Length

Max length28
Median length19
Mean length11.787007
Min length4

Characters and Unicode

Total characters71665
Distinct characters73
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)1.2%

Sample

1st rowSergio Busquets
2nd rowMarc-André ter Stegen
3rd rowSergio Busquets
4th rowSergio Busquets
5th rowSergio Busquets

Common Values

ValueCountFrequency (%)
Hugo Mallo 205
 
3.4%
Sergio Ramos 155
 
2.5%
Jesús Navas 143
 
2.4%
Daniel Parejo 140
 
2.3%
José Luis Morales 139
 
2.3%
Lionel Messi 135
 
2.2%
Koke 134
 
2.2%
Iker Muniain 133
 
2.2%
Joaquín 124
 
2.0%
Mario Gaspar 122
 
2.0%
Other values (296) 4650
76.5%

Length

2023-08-13T19:49:10.422633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
garcía 363
 
3.1%
sergio 339
 
2.9%
josé 266
 
2.3%
luis 228
 
2.0%
hugo 205
 
1.8%
mallo 205
 
1.8%
lópez 184
 
1.6%
víctor 167
 
1.4%
david 164
 
1.4%
javi 155
 
1.3%
Other values (405) 9379
80.5%

Most occurring characters

ValueCountFrequency (%)
a 7811
 
10.9%
o 5730
 
8.0%
5575
 
7.8%
e 5325
 
7.4%
r 5081
 
7.1%
i 4667
 
6.5%
n 3988
 
5.6%
s 3153
 
4.4%
l 2912
 
4.1%
u 1883
 
2.6%
Other values (63) 25540
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54473
76.0%
Uppercase Letter 11599
 
16.2%
Space Separator 5575
 
7.8%
Dash Punctuation 18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7811
14.3%
o 5730
10.5%
e 5325
9.8%
r 5081
9.3%
i 4667
 
8.6%
n 3988
 
7.3%
s 3153
 
5.8%
l 2912
 
5.3%
u 1883
 
3.5%
d 1374
 
2.5%
Other values (31) 12549
23.0%
Uppercase Letter
ValueCountFrequency (%)
M 1682
14.5%
G 1144
 
9.9%
J 1023
 
8.8%
S 860
 
7.4%
D 774
 
6.7%
A 749
 
6.5%
L 729
 
6.3%
R 639
 
5.5%
P 513
 
4.4%
I 488
 
4.2%
Other values (20) 2998
25.8%
Space Separator
ValueCountFrequency (%)
5575
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66072
92.2%
Common 5593
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7811
 
11.8%
o 5730
 
8.7%
e 5325
 
8.1%
r 5081
 
7.7%
i 4667
 
7.1%
n 3988
 
6.0%
s 3153
 
4.8%
l 2912
 
4.4%
u 1883
 
2.8%
M 1682
 
2.5%
Other values (61) 23840
36.1%
Common
ValueCountFrequency (%)
5575
99.7%
- 18
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68747
95.9%
None 2918
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7811
 
11.4%
o 5730
 
8.3%
5575
 
8.1%
e 5325
 
7.7%
r 5081
 
7.4%
i 4667
 
6.8%
n 3988
 
5.8%
s 3153
 
4.6%
l 2912
 
4.2%
u 1883
 
2.7%
Other values (44) 22622
32.9%
None
ValueCountFrequency (%)
í 866
29.7%
é 639
21.9%
á 400
13.7%
ú 279
 
9.6%
ó 221
 
7.6%
Á 141
 
4.8%
à 89
 
3.1%
Ó 86
 
2.9%
ñ 81
 
2.8%
ć 48
 
1.6%
Other values (9) 68
 
2.3%

formation
Categorical

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
4-4-2
1765 
4-2-3-1
1552 
4-3-3
1101 
4-1-4-1
378 
5-3-2
287 
Other values (17)
997 

Length

Max length10
Median length5
Mean length5.8245066
Min length5

Characters and Unicode

Total characters35413
Distinct characters8
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row4-3-3
2nd row3-2-4-1
3rd row4-3-3
4th row4-3-3
5th row4-3-3

Common Values

ValueCountFrequency (%)
4-4-2 1765
29.0%
4-2-3-1 1552
25.5%
4-3-3 1101
18.1%
4-1-4-1 378
 
6.2%
5-3-2 287
 
4.7%
3-4-3 229
 
3.8%
3-5-2 133
 
2.2%
5-4-1 121
 
2.0%
4-4-1-1 103
 
1.7%
4-1-3-2 100
 
1.6%
Other values (12) 311
 
5.1%

Length

2023-08-13T19:49:10.517957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4-4-2 1765
29.0%
4-2-3-1 1552
25.5%
4-3-3 1101
18.1%
4-1-4-1 378
 
6.2%
5-3-2 287
 
4.7%
3-4-3 229
 
3.8%
3-5-2 133
 
2.2%
5-4-1 121
 
2.0%
4-4-1-1 103
 
1.7%
4-1-3-2 100
 
1.6%
Other values (12) 311
 
5.1%

Most occurring characters

ValueCountFrequency (%)
- 14630
41.3%
4 7896
22.3%
3 4934
 
13.9%
2 4183
 
11.8%
1 3108
 
8.8%
5 588
 
1.7%
73
 
0.2%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20710
58.5%
Dash Punctuation 14630
41.3%
Other Symbol 73
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7896
38.1%
3 4934
23.8%
2 4183
20.2%
1 3108
 
15.0%
5 588
 
2.8%
0 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 14630
100.0%
Other Symbol
ValueCountFrequency (%)
73
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 35413
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 14630
41.3%
4 7896
22.3%
3 4934
 
13.9%
2 4183
 
11.8%
1 3108
 
8.8%
5 588
 
1.7%
73
 
0.2%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35340
99.8%
Geometric Shapes 73
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 14630
41.4%
4 7896
22.3%
3 4934
 
14.0%
2 4183
 
11.8%
1 3108
 
8.8%
5 588
 
1.7%
0 1
 
< 0.1%
Geometric Shapes
ValueCountFrequency (%)
73
100.0%

referee
Categorical

Distinct40
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
José Sánchez
 
320
Ricardo de Burgos
 
312
Carlos del Cerro
 
312
Jesús Gil
 
312
Juan Martínez
 
298
Other values (35)
4526 

Length

Max length24
Median length16
Mean length14.500987
Min length9

Characters and Unicode

Total characters88166
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlejandro Hernández
2nd rowJosé Luis Munuera
3rd rowRicardo de Burgos
4th rowAntonio Matéu Lahoz
5th rowCarlos del Cerro

Common Values

ValueCountFrequency (%)
José Sánchez 320
 
5.3%
Ricardo de Burgos 312
 
5.1%
Carlos del Cerro 312
 
5.1%
Jesús Gil 312
 
5.1%
Juan Martínez 298
 
4.9%
Alejandro Hernández 292
 
4.8%
Mario Melero 286
 
4.7%
Antonio Matéu Lahoz 276
 
4.5%
Santiago Jaime 270
 
4.4%
Javier Estrada 236
 
3.9%
Other values (30) 3166
52.1%

Length

2023-08-13T19:49:10.616248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
josé 790
 
5.8%
gonzález 438
 
3.2%
carlos 430
 
3.1%
de 426
 
3.1%
alejandro 366
 
2.7%
juan 338
 
2.5%
sánchez 320
 
2.3%
cerro 312
 
2.3%
jesús 312
 
2.3%
gil 312
 
2.3%
Other values (61) 9640
70.4%

Most occurring characters

ValueCountFrequency (%)
o 7834
 
8.9%
a 7664
 
8.7%
7604
 
8.6%
e 7122
 
8.1%
r 6808
 
7.7%
n 5046
 
5.7%
i 4618
 
5.2%
l 4490
 
5.1%
s 3718
 
4.2%
d 3590
 
4.1%
Other values (39) 29672
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67616
76.7%
Uppercase Letter 12946
 
14.7%
Space Separator 7604
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 7834
11.6%
a 7664
11.3%
e 7122
10.5%
r 6808
10.1%
n 5046
 
7.5%
i 4618
 
6.8%
l 4490
 
6.6%
s 3718
 
5.5%
d 3590
 
5.3%
z 2752
 
4.1%
Other values (17) 13974
20.7%
Uppercase Letter
ValueCountFrequency (%)
J 2098
16.2%
M 1834
14.2%
A 1378
10.6%
C 1320
10.2%
G 976
 
7.5%
S 752
 
5.8%
P 596
 
4.6%
R 540
 
4.2%
I 502
 
3.9%
L 494
 
3.8%
Other values (11) 2456
19.0%
Space Separator
ValueCountFrequency (%)
7604
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 80562
91.4%
Common 7604
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 7834
 
9.7%
a 7664
 
9.5%
e 7122
 
8.8%
r 6808
 
8.5%
n 5046
 
6.3%
i 4618
 
5.7%
l 4490
 
5.6%
s 3718
 
4.6%
d 3590
 
4.5%
z 2752
 
3.4%
Other values (38) 26920
33.4%
Common
ValueCountFrequency (%)
7604
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84088
95.4%
None 4078
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 7834
 
9.3%
a 7664
 
9.1%
7604
 
9.0%
e 7122
 
8.5%
r 6808
 
8.1%
n 5046
 
6.0%
i 4618
 
5.5%
l 4490
 
5.3%
s 3718
 
4.4%
d 3590
 
4.3%
Other values (32) 25594
30.4%
None
ValueCountFrequency (%)
é 1428
35.0%
á 1314
32.2%
í 626
15.4%
ú 312
 
7.7%
Á 172
 
4.2%
ñ 150
 
3.7%
ó 76
 
1.9%

match report
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
Match Report
6080 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters72960
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMatch Report
2nd rowMatch Report
3rd rowMatch Report
4th rowMatch Report
5th rowMatch Report

Common Values

ValueCountFrequency (%)
Match Report 6080
100.0%

Length

2023-08-13T19:49:10.709582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:49:10.792899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
match 6080
50.0%
report 6080
50.0%

Most occurring characters

ValueCountFrequency (%)
t 12160
16.7%
M 6080
8.3%
a 6080
8.3%
c 6080
8.3%
h 6080
8.3%
6080
8.3%
R 6080
8.3%
e 6080
8.3%
p 6080
8.3%
o 6080
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54720
75.0%
Uppercase Letter 12160
 
16.7%
Space Separator 6080
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 12160
22.2%
a 6080
11.1%
c 6080
11.1%
h 6080
11.1%
e 6080
11.1%
p 6080
11.1%
o 6080
11.1%
r 6080
11.1%
Uppercase Letter
ValueCountFrequency (%)
M 6080
50.0%
R 6080
50.0%
Space Separator
ValueCountFrequency (%)
6080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66880
91.7%
Common 6080
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 12160
18.2%
M 6080
9.1%
a 6080
9.1%
c 6080
9.1%
h 6080
9.1%
R 6080
9.1%
e 6080
9.1%
p 6080
9.1%
o 6080
9.1%
r 6080
9.1%
Common
ValueCountFrequency (%)
6080
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72960
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 12160
16.7%
M 6080
8.3%
a 6080
8.3%
c 6080
8.3%
h 6080
8.3%
6080
8.3%
R 6080
8.3%
e 6080
8.3%
p 6080
8.3%
o 6080
8.3%

notes
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6080
Missing (%)100.0%
Memory size95.0 KiB

sh
Real number (ℝ)

Distinct36
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.65625
Minimum0
Maximum36
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:10.872334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q314
95-th percentile20
Maximum36
Range36
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7474734
Coefficient of variation (CV)0.40728994
Kurtosis0.70509384
Mean11.65625
Median Absolute Deviation (MAD)3
Skewness0.64605538
Sum70870
Variance22.538503
MonotonicityNot monotonic
2023-08-13T19:49:10.972643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
9 544
 
8.9%
10 538
 
8.8%
11 495
 
8.1%
12 472
 
7.8%
13 465
 
7.6%
8 459
 
7.5%
14 439
 
7.2%
7 373
 
6.1%
15 331
 
5.4%
6 328
 
5.4%
Other values (26) 1636
26.9%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 9
 
0.1%
2 20
 
0.3%
3 89
 
1.5%
4 140
 
2.3%
5 199
 
3.3%
6 328
5.4%
7 373
6.1%
8 459
7.5%
9 544
8.9%
ValueCountFrequency (%)
36 1
 
< 0.1%
35 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 2
 
< 0.1%
30 4
 
0.1%
29 6
0.1%
28 12
0.2%
27 14
0.2%
26 10
0.2%

sot
Real number (ℝ)

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9664474
Minimum0
Maximum17
Zeros198
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:11.067015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile8
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3339811
Coefficient of variation (CV)0.58843112
Kurtosis1.1691105
Mean3.9664474
Median Absolute Deviation (MAD)2
Skewness0.84400139
Sum24116
Variance5.4474676
MonotonicityNot monotonic
2023-08-13T19:49:11.149306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 1142
18.8%
4 1004
16.5%
2 958
15.8%
5 818
13.5%
1 583
9.6%
6 557
9.2%
7 363
 
6.0%
8 202
 
3.3%
0 198
 
3.3%
9 119
 
2.0%
Other values (8) 136
 
2.2%
ValueCountFrequency (%)
0 198
 
3.3%
1 583
9.6%
2 958
15.8%
3 1142
18.8%
4 1004
16.5%
5 818
13.5%
6 557
9.2%
7 363
 
6.0%
8 202
 
3.3%
9 119
 
2.0%
ValueCountFrequency (%)
17 2
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 4
 
0.1%
13 10
 
0.2%
12 20
 
0.3%
11 39
 
0.6%
10 58
 
1.0%
9 119
2.0%
8 202
3.3%

dist
Real number (ℝ)

Distinct221
Distinct (%)4.8%
Missing1522
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean18.296402
Minimum4.8
Maximum46.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:11.262517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4.8
5-th percentile13.2
Q116.1
median18.2
Q320.2
95-th percentile24
Maximum46.2
Range41.4
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.3633114
Coefficient of variation (CV)0.18382365
Kurtosis2.2547034
Mean18.296402
Median Absolute Deviation (MAD)2.1
Skewness0.61881333
Sum83395
Variance11.311863
MonotonicityNot monotonic
2023-08-13T19:49:11.376730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.4 74
 
1.2%
18.6 71
 
1.2%
18.3 69
 
1.1%
17.3 66
 
1.1%
20.1 66
 
1.1%
19.3 63
 
1.0%
19.5 63
 
1.0%
17.6 62
 
1.0%
18 61
 
1.0%
18.7 60
 
1.0%
Other values (211) 3903
64.2%
(Missing) 1522
 
25.0%
ValueCountFrequency (%)
4.8 1
 
< 0.1%
6.3 1
 
< 0.1%
7 1
 
< 0.1%
8.3 1
 
< 0.1%
8.4 1
 
< 0.1%
8.7 1
 
< 0.1%
8.8 1
 
< 0.1%
9.1 3
< 0.1%
9.3 1
 
< 0.1%
9.5 2
< 0.1%
ValueCountFrequency (%)
46.2 1
< 0.1%
36.9 1
< 0.1%
36.2 1
< 0.1%
35.9 1
< 0.1%
34.8 1
< 0.1%
33.8 1
< 0.1%
33.5 1
< 0.1%
33 1
< 0.1%
31.7 2
< 0.1%
31.4 1
< 0.1%

pk
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
0.0
5370 
1.0
668 
2.0
 
39
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18240
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5370
88.3%
1.0 668
 
11.0%
2.0 39
 
0.6%
3.0 3
 
< 0.1%

Length

2023-08-13T19:49:11.472990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:49:11.562294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5370
88.3%
1.0 668
 
11.0%
2.0 39
 
0.6%
3.0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 11450
62.8%
. 6080
33.3%
1 668
 
3.7%
2 39
 
0.2%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12160
66.7%
Other Punctuation 6080
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11450
94.2%
1 668
 
5.5%
2 39
 
0.3%
3 3
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11450
62.8%
. 6080
33.3%
1 668
 
3.7%
2 39
 
0.2%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11450
62.8%
. 6080
33.3%
1 668
 
3.7%
2 39
 
0.2%
3 3
 
< 0.1%

pkatt
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
0.0
5167 
1.0
835 
2.0
 
72
3.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18240
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5167
85.0%
1.0 835
 
13.7%
2.0 72
 
1.2%
3.0 6
 
0.1%

Length

2023-08-13T19:49:11.642572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-13T19:49:11.731977image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5167
85.0%
1.0 835
 
13.7%
2.0 72
 
1.2%
3.0 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 11247
61.7%
. 6080
33.3%
1 835
 
4.6%
2 72
 
0.4%
3 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12160
66.7%
Other Punctuation 6080
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11247
92.5%
1 835
 
6.9%
2 72
 
0.6%
3 6
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 6080
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11247
61.7%
. 6080
33.3%
1 835
 
4.6%
2 72
 
0.4%
3 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11247
61.7%
. 6080
33.3%
1 835
 
4.6%
2 72
 
0.4%
3 6
 
< 0.1%

season
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.5
Minimum2015
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size95.0 KiB
2023-08-13T19:49:11.806777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016.75
median2018.5
Q32020.25
95-th percentile2022
Maximum2022
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.2914763
Coefficient of variation (CV)0.0011352372
Kurtosis-1.2381265
Mean2018.5
Median Absolute Deviation (MAD)2
Skewness0
Sum12272480
Variance5.2508636
MonotonicityDecreasing
2023-08-13T19:49:11.883185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2022 760
12.5%
2021 760
12.5%
2020 760
12.5%
2019 760
12.5%
2018 760
12.5%
2017 760
12.5%
2016 760
12.5%
2015 760
12.5%
ValueCountFrequency (%)
2015 760
12.5%
2016 760
12.5%
2017 760
12.5%
2018 760
12.5%
2019 760
12.5%
2020 760
12.5%
2021 760
12.5%
2022 760
12.5%
ValueCountFrequency (%)
2022 760
12.5%
2021 760
12.5%
2020 760
12.5%
2019 760
12.5%
2018 760
12.5%
2017 760
12.5%
2016 760
12.5%
2015 760
12.5%

team
Categorical

Distinct30
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size95.0 KiB
Barcelona
 
304
Athletic Club
 
304
Real Madrid
 
304
Celta Vigo
 
304
Sevilla
 
304
Other values (25)
4560 

Length

Max length19
Median length14
Mean length9.2
Min length5

Characters and Unicode

Total characters55936
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBarcelona
2nd rowBarcelona
3rd rowBarcelona
4th rowBarcelona
5th rowBarcelona

Common Values

ValueCountFrequency (%)
Barcelona 304
 
5.0%
Athletic Club 304
 
5.0%
Real Madrid 304
 
5.0%
Celta Vigo 304
 
5.0%
Sevilla 304
 
5.0%
Valencia 304
 
5.0%
Real Betis 304
 
5.0%
Villarreal 304
 
5.0%
Real Sociedad 304
 
5.0%
Atletico Madrid 304
 
5.0%
Other values (20) 3040
50.0%

Length

2023-08-13T19:49:11.981506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
real 912
 
10.8%
madrid 608
 
7.2%
barcelona 304
 
3.6%
valencia 304
 
3.6%
atletico 304
 
3.6%
sociedad 304
 
3.6%
villarreal 304
 
3.6%
athletic 304
 
3.6%
betis 304
 
3.6%
sevilla 304
 
3.6%
Other values (28) 4522
53.4%

Most occurring characters

ValueCountFrequency (%)
a 8816
15.8%
l 6118
 
10.9%
e 5662
 
10.1%
i 3952
 
7.1%
o 2660
 
4.8%
t 2508
 
4.5%
r 2508
 
4.5%
d 2432
 
4.3%
2394
 
4.3%
n 2166
 
3.9%
Other values (27) 16720
29.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 45068
80.6%
Uppercase Letter 8474
 
15.1%
Space Separator 2394
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 8816
19.6%
l 6118
13.6%
e 5662
12.6%
i 3952
8.8%
o 2660
 
5.9%
t 2508
 
5.6%
r 2508
 
5.6%
d 2432
 
5.4%
n 2166
 
4.8%
c 1976
 
4.4%
Other values (12) 6270
13.9%
Uppercase Letter
ValueCountFrequency (%)
V 1216
14.3%
R 1064
12.6%
A 874
10.3%
C 836
9.9%
M 836
9.9%
S 684
8.1%
G 646
7.6%
L 608
7.2%
E 608
7.2%
B 608
7.2%
Other values (4) 494
5.8%
Space Separator
ValueCountFrequency (%)
2394
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53542
95.7%
Common 2394
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 8816
16.5%
l 6118
11.4%
e 5662
 
10.6%
i 3952
 
7.4%
o 2660
 
5.0%
t 2508
 
4.7%
r 2508
 
4.7%
d 2432
 
4.5%
n 2166
 
4.0%
c 1976
 
3.7%
Other values (26) 14744
27.5%
Common
ValueCountFrequency (%)
2394
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 8816
15.8%
l 6118
 
10.9%
e 5662
 
10.1%
i 3952
 
7.1%
o 2660
 
4.8%
t 2508
 
4.5%
r 2508
 
4.5%
d 2432
 
4.3%
2394
 
4.3%
n 2166
 
3.9%
Other values (27) 16720
29.9%

Interactions

2023-08-13T19:49:06.033189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:54.724221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.435570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.445103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.545907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.534827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.705215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.871799image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.903231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.984277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:06.132436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:54.860379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.524811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.547410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.657133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.628090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.806460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.967069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.998529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.126945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:06.233682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:54.957633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.613093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.647661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.764490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.722384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.899213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.063357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.093897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.226195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:06.324952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:55.056882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.708462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.744914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.853805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.818743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.990484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.164574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.191150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.309973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:06.423203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:55.156131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.806715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.843171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.946169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.909985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.088740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.256845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.285410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.396257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:06.526547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:55.264355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.926914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.957386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.048412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.008282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.191986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.355099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.385657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.490529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:06.625798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.027475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.028157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.090545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.144675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.105539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.312695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.451362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.480921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.589786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:06.952940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.132705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.129401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.210259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.241927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.413375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.442870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.553605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.583266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.683054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:07.056268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.242923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.239621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.340428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.347223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.516650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.601965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.661831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.695485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.816733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:07.152492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:57.332232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:58.336875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:48:59.443667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:00.439527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:01.602423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:02.736642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:03.790008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:04.825183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-08-13T19:49:05.938925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Missing values

2023-08-13T19:49:07.342209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-13T19:49:07.765400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-13T19:49:07.950073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datetimecomprounddayvenueresultgfgaopponentxgxgapossattendancecaptainformationrefereematch reportnotesshsotdistpkpkattseasonteam
02022-08-1321:00La LigaMatchweek 1SatHomeD0.00.0Rayo Vallecano1.90.567.081104.0Sergio Busquets4-3-3Alejandro HernándezMatch ReportNaN21.05.017.00.00.02022Barcelona
12022-08-2122:00La LigaMatchweek 2SunAwayW4.01.0Real Sociedad2.10.858.036201.0Marc-André ter Stegen3-2-4-1José Luis MunueraMatch ReportNaN15.07.014.60.00.02022Barcelona
22022-08-2819:30La LigaMatchweek 3SunHomeW4.00.0Valladolid2.60.867.083972.0Sergio Busquets4-3-3Ricardo de BurgosMatch ReportNaN24.09.014.40.00.02022Barcelona
32022-09-0321:00La LigaMatchweek 4SatAwayW3.00.0Sevilla4.01.054.040233.0Sergio Busquets4-3-3Antonio Matéu LahozMatch ReportNaN18.05.016.00.00.02022Barcelona
52022-09-1018:30La LigaMatchweek 5SatAwayW4.00.0Cádiz3.40.470.019530.0Sergio Busquets4-3-3Carlos del CerroMatch ReportNaN16.08.014.90.00.02022Barcelona
72022-09-1716:15La LigaMatchweek 6SatHomeW3.00.0Elche3.90.076.085073.0Marc-André ter Stegen4-3-3Alejandro MuñízMatch ReportNaN25.09.015.00.00.02022Barcelona
82022-10-0121:00La LigaMatchweek 7SatAwayW1.00.0Mallorca0.71.271.018103.0Sergio Busquets4-3-3Jesús GilMatch ReportNaN11.03.018.70.00.02022Barcelona
102022-10-0921:00La LigaMatchweek 8SunHomeW1.00.0Celta Vigo2.01.557.081525.0Sergio Busquets4-3-3José Luis MunueraMatch ReportNaN13.05.014.10.00.02022Barcelona
122022-10-1616:15La LigaMatchweek 9SunAwayL1.03.0Real Madrid2.01.156.062876.0Sergio Busquets4-3-3José SánchezMatch ReportNaN18.05.015.60.00.02022Barcelona
132022-10-2021:00La LigaMatchweek 10ThuHomeW3.00.0Villarreal2.10.469.073261.0Sergi Roberto4-3-3Carlos del CerroMatch ReportNaN16.05.014.20.00.02022Barcelona
datetimecomprounddayvenueresultgfgaopponentxgxgapossattendancecaptainformationrefereematch reportnotesshsotdistpkpkattseasonteam
302016-03-1312:00La LigaMatchweek 29SunHomeW1.00.0ValenciaNaNNaN46.018191.0Diego Mariño4-4-2Carlos del CerroMatch ReportNaN13.06.0NaN0.00.02015Levante
312016-03-1920:30La LigaMatchweek 30SatAwayL1.02.0La CoruñaNaNNaN47.024920.0Juanfran4-4-2Iñaki BikandiMatch ReportNaN7.01.0NaN0.00.02015Levante
322016-04-0420:30La LigaMatchweek 31MonHomeD0.00.0Sporting GijónNaNNaN54.011302.0Juanfran4-4-2Mario MeleroMatch ReportNaN16.04.0NaN0.00.02015Levante
332016-04-0922:05La LigaMatchweek 32SatAwayL0.01.0BetisNaNNaN49.037091.0Juanfran4-4-2Eduardo PrietoMatch ReportNaN12.03.0NaN0.00.02015Levante
342016-04-1520:30La LigaMatchweek 33FriHomeW2.01.0EspanyolNaNNaN51.016192.0Juanfran4-4-2Alejandro HernándezMatch ReportNaN12.04.0NaN0.00.02015Levante
352016-04-2121:00La LigaMatchweek 34ThuAwayL1.05.0GranadaNaNNaN52.021131.0David Navarro4-4-2Santiago JaimeMatch ReportNaN15.05.0NaN0.00.02015Levante
362016-04-2412:00La LigaMatchweek 35SunHomeD2.02.0Athletic ClubNaNNaN36.015263.0Juanfran4-4-2Ignacio IglesiasMatch ReportNaN10.05.0NaN0.00.02015Levante
372016-05-0220:30La LigaMatchweek 36MonAwayL1.03.0MálagaNaNNaN42.017562.0Juanfran4-4-2Iñaki BikandiMatch ReportNaN11.05.0NaN0.00.02015Levante
382016-05-0817:00La LigaMatchweek 37SunHomeW2.01.0Atlético MadridNaNNaN55.012054.0Juanfran4-2-3-1José GonzálezMatch ReportNaN11.05.0NaN0.00.02015Levante
392016-05-1519:30La LigaMatchweek 38SunAwayL1.03.0Rayo VallecanoNaNNaN44.012547.0Juanfran4-2-3-1Carlos Clos GómezMatch ReportNaN8.02.0NaN1.01.02015Levante